Linking Marine Fog Variability in Atlantic Canada to Changes in Large-Scale Atmospheric and Marine Features
Bibliographic record
Abstract
Marine fog varies on annual, decadal, and climate change scales, with implications on transportation and the global radiative budget. Using reanalysis and airport meteorological data from 1953 to 2019, this study investigates these long-term variations along the Canadian Atlantic coast and its underlying drivers. A shift in dominant drivers is observed in the early 1990s: prior to that, sea-level pressure moderately correlated with annual fog at Sable Island (R = 0.58, p < 0.001), whereas sea-surface temperature (SST) became the primary influence afterward, with a significant negative correlation (R = −0.55, p = 0.003). This change coincides with a rapid warming of SST along the Scotian Shelf, which reduced the air–sea temperature contrast necessary for fog formation. Annual fog frequency also declined significantly over time, with trends of −25 to −45 h per decade across the six coastal stations studied. These trends were most pronounced in the foggiest period of the year: spring and summer. In addition to ocean warming, a weakening of near-surface temperature inversions and long-term rise in boundary layer height (BLH) suggest reduced atmospheric stability as a key mechanism limiting fog formation. These stability indicators co-vary with fog on interannual timescales and reinforce the role of stratification in supporting marine fog. This study highlights the evolving role of fog drivers in a changing climate and offers a physical basis to improve future fog projections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".